GeFaST: An improved method for OTU assignment by generalising Swarm’s fastidious clustering approach
Abstract Background Massive genomic data sets from high-throughput sequencing allow for new insights into complex biological systems such as microbial communities. Analyses of their diversity and structure are typically preceded by clustering millions of 16S rRNA gene sequences into OTUs. Swarm intr...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-09-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2349-1 |
| _version_ | 1819138011290402816 |
|---|---|
| author | Robert Müller Markus E. Nebel |
| author_facet | Robert Müller Markus E. Nebel |
| author_sort | Robert Müller |
| collection | DOAJ |
| description | Abstract Background Massive genomic data sets from high-throughput sequencing allow for new insights into complex biological systems such as microbial communities. Analyses of their diversity and structure are typically preceded by clustering millions of 16S rRNA gene sequences into OTUs. Swarm introduced a new clustering strategy which addresses important conceptual and performance issues of the popular de novo clustering approach. However, some parts of the new strategy, e.g. the fastidious option for increased clustering quality, come with their own restrictions. Results In this paper, we present the new exact, alignment-based de novo clustering tool GeFaST, which implements a generalisation of Swarm’s fastidious clustering. Our tool extends the fastidious option to arbitrary clustering thresholds and allows to adjust its greediness. GeFaST was evaluated on mock-community and natural data and achieved higher clustering quality and performance for small to medium clustering thresholds compared to Swarm and other de novo tools. Clustering with GeFaST was between 6 and 197 times as fast as with Swarm, while the latter required up to 38% less memory for non-fastidious clustering but at least three times as much memory for fastidious clustering. Conclusions GeFaST extends the scope of Swarm’s clustering strategy by generalising its fastidious option, thereby allowing for gains in clustering quality, and by increasing its performance (especially in the fastidious case). Our evaluations showed that GeFaST has the potential to leverage the use of the (fastidious) clustering strategy for higher thresholds and on larger data sets. |
| first_indexed | 2024-12-22T10:59:59Z |
| format | Article |
| id | doaj.art-130d8ce051684a9abd42f322d8c44a3f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-22T10:59:59Z |
| publishDate | 2018-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-130d8ce051684a9abd42f322d8c44a3f2022-12-21T18:28:32ZengBMCBMC Bioinformatics1471-21052018-09-0119111410.1186/s12859-018-2349-1GeFaST: An improved method for OTU assignment by generalising Swarm’s fastidious clustering approachRobert Müller0Markus E. Nebel1International Research Training Group “Computational Methods for the Analysis of the Diversity and Dynamics of Genomes”, Bielefeld UniversityInternational Research Training Group “Computational Methods for the Analysis of the Diversity and Dynamics of Genomes”, Bielefeld UniversityAbstract Background Massive genomic data sets from high-throughput sequencing allow for new insights into complex biological systems such as microbial communities. Analyses of their diversity and structure are typically preceded by clustering millions of 16S rRNA gene sequences into OTUs. Swarm introduced a new clustering strategy which addresses important conceptual and performance issues of the popular de novo clustering approach. However, some parts of the new strategy, e.g. the fastidious option for increased clustering quality, come with their own restrictions. Results In this paper, we present the new exact, alignment-based de novo clustering tool GeFaST, which implements a generalisation of Swarm’s fastidious clustering. Our tool extends the fastidious option to arbitrary clustering thresholds and allows to adjust its greediness. GeFaST was evaluated on mock-community and natural data and achieved higher clustering quality and performance for small to medium clustering thresholds compared to Swarm and other de novo tools. Clustering with GeFaST was between 6 and 197 times as fast as with Swarm, while the latter required up to 38% less memory for non-fastidious clustering but at least three times as much memory for fastidious clustering. Conclusions GeFaST extends the scope of Swarm’s clustering strategy by generalising its fastidious option, thereby allowing for gains in clustering quality, and by increasing its performance (especially in the fastidious case). Our evaluations showed that GeFaST has the potential to leverage the use of the (fastidious) clustering strategy for higher thresholds and on larger data sets.http://link.springer.com/article/10.1186/s12859-018-2349-1Sequence clusteringOperational taxonomic unitsMicrobial community analysis |
| spellingShingle | Robert Müller Markus E. Nebel GeFaST: An improved method for OTU assignment by generalising Swarm’s fastidious clustering approach BMC Bioinformatics Sequence clustering Operational taxonomic units Microbial community analysis |
| title | GeFaST: An improved method for OTU assignment by generalising Swarm’s fastidious clustering approach |
| title_full | GeFaST: An improved method for OTU assignment by generalising Swarm’s fastidious clustering approach |
| title_fullStr | GeFaST: An improved method for OTU assignment by generalising Swarm’s fastidious clustering approach |
| title_full_unstemmed | GeFaST: An improved method for OTU assignment by generalising Swarm’s fastidious clustering approach |
| title_short | GeFaST: An improved method for OTU assignment by generalising Swarm’s fastidious clustering approach |
| title_sort | gefast an improved method for otu assignment by generalising swarm s fastidious clustering approach |
| topic | Sequence clustering Operational taxonomic units Microbial community analysis |
| url | http://link.springer.com/article/10.1186/s12859-018-2349-1 |
| work_keys_str_mv | AT robertmuller gefastanimprovedmethodforotuassignmentbygeneralisingswarmsfastidiousclusteringapproach AT markusenebel gefastanimprovedmethodforotuassignmentbygeneralisingswarmsfastidiousclusteringapproach |